The invention concerns a method for coding a sequence of digital images as well as a corresponding method for decoding. Furthermore, the invention refers to a coding apparatus and a decoding apparatus as well as a system for coding and decoding a sequence of images.
For a fast and efficient transmission and storage of images, compression algorithms are used in order to exploit the spatial, temporal and probabilistic redundancies in the images. In video coding, block-based motion compensation techniques are known which seek to reduce the spatial variations in using a block-based transitional motion prediction and compensation in time direction. To do so, the motion of predefined blocks comprising several pixels in a current image relative to the previous image in an image sequence is determined, minimizing an intensity difference norm. Using this motion information for prediction, only the residual error, which contains much less variation, has to be transmitted in order to obtain a lossless reconstruction at the decoder. Block-based methods have the disadvantage of generating block artifacts at block boundaries in the decoded image. Several improvements of block-based motion compensation are described in the related art, e.g. an adapting block-size, adaptive choice of intra-coding, weighted prediction from multiple preceding and succeeding image, in-loop filtering, and so on.
Besides block-based motion compensation, there are also approaches for pixel-based motion compensation. By this motion compensation, an arbitrary motion can be described using pixel-based motion vector fields. These methods have the disadvantage that due to the large number of motion vectors, much side information has to be transmitted to the decoder. In order to reduce this side information, the reference “Sparse representation of dense motion vector fields for lossless compression of 4-D medical CT data,” by A. Weinlich et al. (EUSIPCO, 2011) describes a sparse representation of dense motion vector fields where only significant motion vectors with respect to their prediction capability are coded.